Non-parametric confidence bounds for process performance monitoring charts

Abstract

Statistical Process Control (SPC) provides a tool for achieving and maintaining product quality. In today's climate of major data monitoring campaigns there has been an increase in interest in the multivariate statistical projection techniques of principal components analysis and projection to latent structures for process performance monitoring. Within univariate SPC, techniques for identifying when a process is moving out of control are well established. Similar guidelines are required for multivariate statistical process control (MSPC). Two approaches will be discussed - Hotelling's T2 statistic and a new approach, the M2 statistic. Both approaches will be illustrated by application to a high pressure low density polyethylene tubular reactor and to a batch methyl methacrylate polymerisation reactor.